CN110044693A - A kind of sensor states method of real-time for structure load electrical testing inspection - Google Patents
A kind of sensor states method of real-time for structure load electrical testing inspection Download PDFInfo
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Abstract
The present invention provides a kind of sensor states method of real-time for structure load electrical testing inspection, belongs to test mechanics field.The monitoring method are as follows: firstly, sensor is numbered and is classified before structure load, then preload, sensor abnormality is judged automatically according to measurement data and sensor is adjusted by testing crew；Secondly, confirmation sensor calculates the linearly dependent coefficient between each sensor measured data and load data in real time, analyzes the working condition of each sensor without formally being loaded after adjustment in loading procedure；Finally, sensor states grade is determined, according to respective handling measure Adjustment Tests process according to sensor instantaneous operating conditions and its importance rate.The present invention realizes auto-real-time monitoring to sensor states and alarm in structure load electrical testing inspection, can effective Control experiment risk, improve success of the test rate.
Description
Technical field
The invention belongs to test mechanics field, be related to it is a kind of for structure load electrical testing inspection sensor states supervise in real time
Survey method.
Background technique
In the load electrical testing inspection of structure, generally require in a large amount of foil gauges of structural test part surface layout, displacement meter etc.
Sensor, the data such as strain, displacement for measurement structure privileged site.In the test preparation stage, when sensor breaks down
It is easy to check and repair；However during testing progress, once sensor breaks down, in the feelings for lacking automatic checkout system
Under condition, testing crew is generally difficult to find rapidly and handle, it is most likely that causes experimental data to lose, influences test result even
Lead to test failure.It is therefore desirable to invent a kind of sensor states real-time monitoring side that can be applied during testing and carrying out
Method judges its instantaneous operating conditions by analyzing each sensor institute measured data, and calculates sensing system health value, determines to pass
Sensor state grade；When sensor states occur abnormal, respective handling measure is taken automatically.
Summary of the invention
The purpose of the present invention is realize the real-time monitoring of sensor states in structure load electrical testing inspection, Control experiment wind
Success of the test rate is improved in danger.
In order to achieve the above object, the invention adopts the following technical scheme:
A kind of sensor states method of real-time for structure load electrical testing inspection, comprising the following steps:
Step 1: sensor number and being classified.
During testing preparation, all the sensors are numbered, will be sensed with testing the foundation such as demand, sensor position
Device carries out importance classification, and each sensor number and its importance rate is pre-stored in monitoring system, while in systems
Define the single sensor health weight of each importance rate.
The sensor is divided into three-level according to importance: key area sensor, important area sensor, general area
Sensor.Single sensor health weight uses F according to its importance rate respectivelyP(key area), FI(important area), FG(one
As region) indicate；Sensor importance rate is higher, and healthy weight is higher, and specific value can be according to test requirement definition.
Measurement data is obtained Step 2: preloading.
It preloads, applies load to the 20% of estimated full value, obtain and record sensor measurement data.
Step 3: sensor abnormality judges.
After preloading, by individually calculating the linearly dependent coefficient between each sensor measured data and load, sentence
Each sensor break with the presence or absence of linear abnormal: if linearly dependent coefficient is greater than 0.8, judging the sensor, there is no lines
Sexual abnormality；If it is linear abnormal that linearly dependent coefficient less than 0.8, judges that the sensor occurs.For there is no linear abnormal
Sensor, calculate proportionality coefficient between its measured data and load, obtain actual measurement proportionality coefficient, then it is calculated with numerical value
The index contrast arrived obtains actual measurement scale factor errors (using numerical result as standard): if actual measurement scale factor errors
Greater than 5%, then it is assumed that there are abnormal proportions for sensor；If surveying scale factor errors less than 5%, then it is assumed that sensor is just
Often.
Software kit monitoring interface prompts sensor there are exception and shows that abnormality sensor is numbered, while relevant linear
Abnormality sensor data are shown as black, and abnormal proportion sensing data is shown as brown, and nominal sensor data are shown as
Blue.
Step 4: selecting next step by testing crew according to the sensor abnormality judging result of step 3.If necessary
Sensor is adjusted, then enters step five；If you do not need to adjustment sensor, then start formally to load, enter step six.
Step 5: adjustment abnormality sensor.Two are entered step after adjustment, are preloaded again.
Step 6: formal load, obtains real-time measuring data.
Gradually apply load by test plan, and records sensor real time data.
Step 7: working sensor state analysis.
Monitoring system is according to each sensing data real-time change situation of the real-time data analysis obtained in step 6；It is logical
It crosses using the linearly dependent coefficient calculated with method identical in step 3 between each sensor measured data and load, judgement is every
One sensor states: if linearly dependent coefficient is greater than threshold value, judge that sensor states are normal；If linearly dependent coefficient
Less than threshold value, judge that the sensor breaks down；The threshold size depends on the actual conditions such as test specimen type, loading type.
If there is sensor, institute's measured data reaches its specified range in the case where without failure, then it is assumed that the sensor failure,
Its field data appears dimmed, and no longer calculates the linearly dependent coefficient between its measured data and load；Simultaneously in step
It is not counted in fault sensor in rapid eight, but still is included in sensor sum.
Step 8: judging sensor states grade.
Analysis based on step 7 calculates sensing system health value as a result, count the working condition of each sensor, and
Sensor states grade is determined according to table 1.
1 sensor states of table determine and automatically process Measures Standard
It is required that: the standard for meeting multiple sensor states grades is subject to the highest level reached；Grade number is got over
Small, locating rank is higher；Loading procedure is according to the ratio between load type, the numerical intervals of load, load and specified maximum load
The standards such as section are divided into each loaded segment, according to test demand self-defining；For firsts and seconds sensor states grade, pass
Threshold value S in sensor system health value criterion11、S12、S13、S21、S22、S23... it can be according to test demand in mating prison
Control self-defining in software；Wherein, the S11、S12、S13、S21、S22、S23... it is respectively less than 100；And there is S11≥S12≥S13,
S21≥S22≥S23, S11<S21, S12<S22, S13<S23。
Step 9: according to sensor states grade, Adjustment Tests process.
If sensor states grade is level-one, software kit monitoring interface prompt sensor states grade simultaneously shows event
Hinder sensor number, associated sensor data column number is simultaneously emitted by level-one sound-light alarm, pilot system is immediately according to shown in red
It is automatically stopped load.If sensor states grade is second level, software kit monitoring interface prompts sensor states grade simultaneously
Show fault sensor number, associated sensor data column number is simultaneously emitted by second level sound-light alarm, test system according to being shown as orange
System continues to load.If sensor states grade is three-level, software kit monitoring interface prompt sensor states grade is simultaneously shown
Show that fault sensor is numbered, according to yellow is shown as, pilot system continues to load associated sensor data column number.If sensor shape
State grade is level Four, then software kit monitoring interface is normal with blue font prompt sensor states, sensing data column number evidence
It is shown as blue, pilot system continues to load.When sensor states grade is second level, three-level or level Four, if need to stop testing,
Then it is manually operated by testing crew.Specific sensor states grade and pilot system automatically process measure and are shown in Table 1.
Step 3: linearly dependent coefficient described in step 7 is calculated by following calculation formula:
Wherein, X is that all measured datas of a sensor are sequentially arranged the vector of composition, and Y is experiment load
It is sequentially arranged the vector of composition；Cov (X, Y) is X, and the covariance of Y, Var [X], Var [Y] are respectively X, the variance of Y.
Actual measurement scale factor errors described in step 3 are calculated by following calculation formula:
Sensing system health value described in step 8 is calculated by following calculation formula:
Sensing system health value=100- (FP×NP+FI×NI+FG×NG)
Wherein, FP、FI、FGRespectively represent single key area sensor, important area sensor, general area sensor
Healthy weight；NP、NI、NGRespectively represent the failure of key area sensor, important area sensor, general area sensor
Quantity.
Compared with prior art, the beneficial effects of the present invention are:
For the calculating of linearly dependent coefficient, actual measurement scale factor errors, sensing system health value in this monitoring method
Required computing resource is few, time-consuming short, therefore computer can be rapidly completed, and realize real-time monitoring.Entire monitoring method can be by
Computer is realized automatically, does not need additional ancillary equipment, thus this method implement it is simple and fast, have very strong practicability and
Wide application prospect.
Detailed description of the invention
Fig. 1 is a kind of workflow of the sensor states method of real-time for structure load electrical testing inspection of the present invention
Figure.
Specific embodiment
Specific implementation step of the invention is described further below in conjunction with example.
Structure-oriented of the present invention loads electrometric experiment, it is therefore an objective to realize the reality of sensor states in structure load electrical testing inspection
When monitor, Control experiment risk, improve success of the test rate.Using the pressure bearing test of certain big opening reinforcement barrel shell axis as example, make a reservation for
Testing maximum axial pressure is 40 tons, and the strain using electric measuring system measurement structure portion is needed in loading procedure, is used
Sensor states in real-time monitoring loading procedure of the present invention.
In order to achieve the above object, the present invention specifically includes the following steps:
Step 1: sensor number and being classified.Test prepare during, all the sensors are numbered, and with test demand,
Sensor position etc. carries out importance classification according to by sensor, and each sensor number and its importance rate are prestored
In monitoring system, while the single sensor health weight of each importance rate is defined in systems.Sensor is according to important
Property is divided into three-level: key area sensor, important area sensor, general area sensor.Single sensor health weight
F is used respectively according to its importance rateP(key area), FI(important area), FG(general area) indicates；Sensor importance
Higher grade, and healthy weight is higher.The force snesor of loading device is defined as 1. number sensor, importance rate FPIt (closes
Key range), healthy weight 60；Four foil gauges of aperture position are respectively 2.~5. number sensor, importance rate FI(weight
Want region), the healthy weight 20 of each sensor；5, other regions foil gauge is respectively 6.~10. number sensor, importance etc.
Grade is FG(general area), the healthy weight 5 of each sensor.
Measurement data is obtained Step 2: preloading.It preloads, applies 20% i.e. 8 tons of load to estimated full value, obtain
And record sensor measurement data.
Step 3: sensor abnormality judges.After preloading, by individually calculating each sensor measured data and load
Between linearly dependent coefficient, judge each sensor with the presence or absence of linear abnormal；If linearly dependent coefficient is greater than 0.8,
Then judging sensor, there is no linear abnormal；If it is linear different that linearly dependent coefficient less than 0.8, judges that the sensor occurs
Often；For calculating proportionality coefficient between its measured data and load, and calculate with numerical value there is no linear abnormal sensor
This index contrast arrived calculates actual measurement scale factor errors (using numerical result as standard)；If surveying proportionality coefficient
Error is greater than 5%, then it is assumed that there are abnormal proportions for sensor；If surveying scale factor errors less than 5%, then it is assumed that sensor
Normally.Software kit monitoring interface prompts sensor there are exception and shows that abnormality sensor is numbered, while relevant linear different
Normal sensing data is shown as black, and abnormal proportion sensing data is shown as brown, and nominal sensor data are shown as blue
Color.
Step 4: selecting next step by testing crew according to the sensor abnormality judging result of step 3.If necessary
Sensor is adjusted, then enters step five；If you do not need to adjustment sensor, then start formally to load, enter step six.
Step 5: adjustment abnormality sensor.Two are entered step after adjustment, are preloaded again.
Step 6: formal load, obtains real-time measuring data.Gradually apply load by test plan, and records sensor
Real time data.Loading procedure is divided into 3 loaded segments, wherein the first loaded segment load is the 0-20% of maximum load, both load zones
Between be 0-8 tons；Second loaded segment load is the 20-80% of maximum load, and both load section was 8-32 tons；Third loaded segment load
For the 80-100% of maximum load, both load section was 32-40 tons.
Step 7: working sensor state analysis.Monitoring system analyzes each biography according to the data obtained in step 6
Sensor data real-time change situation；By using with method identical in step 3 calculate each sensor measured data and load it
Between linearly dependent coefficient, judge each sensor states；If linearly dependent coefficient is greater than threshold value, sensor shape is judged
State is normal；If linearly dependent coefficient is less than threshold value, judge that (maximum load is in structure lines in this example for sensor failure
Property carrying range in, theoretically each linearly dependent coefficient is larger, therefore 0.7) structure threshold size is taken as.If there is sensor
Institute's measured data reaches its specified range in the case where without failure, then it is assumed that the sensor failure, field data are aobvious
It is shown as grey, and no longer calculates the linearly dependent coefficient between its measured data and load；Event is not counted in step 8 simultaneously
Hinder sensor, but still is included in sensor sum.
Step 8: judging sensor states grade.Analysis based on step 7 is as a result, count the work shape of each sensor
State calculates sensing system health value, and determines sensor states grade according to table 2.
Step 9: according to sensor states grade, Adjustment Tests process.If sensor states grade is level-one, mating
Software supervision interface prompt sensor states grade simultaneously shows that fault sensor is numbered, and associated sensor data column number evidence is shown as
Red, is simultaneously emitted by level-one sound-light alarm, and pilot system is automatically stopped load immediately；If sensor states grade is second level,
Then software kit monitoring interface prompts sensor states grade and shows that fault sensor is numbered, associated sensor data column number evidence
It is shown as orange, is simultaneously emitted by second level sound-light alarm, pilot system continues to load；If sensor states grade is three-level,
Software kit monitoring interface prompt sensor states grade simultaneously shows that fault sensor is numbered, and associated sensor data column number is according to aobvious
It is shown as yellow, pilot system continues to load.If sensor states grade is level Four, software kit monitoring interface is with blue word
Body prompts sensor states normal, and sensing data column number continues to load according to blue, pilot system is shown as.Sensor states etc.
When grade is second level, three-level or level Four, if need to stop testing, it is manually operated by testing crew.Specific sensor states grade
Measure, which is automatically processed, with pilot system is shown in Table 2.In this example, it is assumed that when magnitude of load is 7 tons, is in the first loaded segment, 1. number
Sensor breaks down, and system health value is 40 at this time, and sensor states grade is level-one, and system makes corresponding response；Assuming that
Magnitude of load is 30 tons, is in the second loaded segment, 4. number 5. number sensor failure, and system health value is 60 at this time, sensing
Device state grade second level, system make corresponding response；Assuming that magnitude of load is 35 tons, is in third loaded segment, 8. number sensor
It breaks down, system health value is 95 at this time, and sensor states grade is three-level, and system makes corresponding response.
Step 3: linearly dependent coefficient described in step 7 is calculated by following calculation formula:
Wherein, X is that all measured datas of a sensor are sequentially arranged the vector of composition, and Y is experiment load
It is sequentially arranged the vector of composition；Cov (X, Y) is X, and the covariance of Y, Var [X], Var [Y] are respectively X, the variance of Y.
Actual measurement scale factor errors described in step 3 are calculated by following calculation formula:
Sensing system health value described in step 8 is calculated by following calculation formula:
Sensing system health value=100- (FP×NP+FI×NI+FG×NG)
Wherein, FP、FI、FGRespectively represent single key area sensor, important area sensor, general area sensor
Healthy weight；NP、NI、NGRespectively represent the failure of key area sensor, important area sensor, general area sensor
Quantity.
2 sensor states of table determine and automatically process Measures Standard
Embodiment described above only expresses embodiments of the present invention, and but it cannot be understood as to the invention patent
Range limitation, it is noted that for those skilled in the art, without departing from the inventive concept of the premise, also
Several modifications and improvements can be made, these are all belonged to the scope of protection of the present invention.
Claims (5)
1. a kind of sensor states method of real-time for structure load electrical testing inspection, which is characterized in that including following step
It is rapid:
Step 1: sensor number and being classified；
Test prepare during, all the sensors are numbered, with test demand, sensor position etc. according to by sensor into
The classification of row importance, and each sensor number and its importance rate are pre-stored in monitoring system, while being defined in systems
The single sensor health weight of each importance rate；
The sensor is divided into three-level according to importance: key area sensor, important area sensor, general area sensing
Device；Single sensor health weight uses F according to its importance rate respectivelyPIndicate key area, FIIndicate important area, FGTable
Show general area；Sensor importance rate is higher, and healthy weight is higher；
Measurement data is obtained Step 2: preloading；
It preloads, applies load to the 20% of estimated full value, obtain and record sensor measurement data；
Step 3: sensor abnormality judges；
After preloading, by individually calculating the linearly dependent coefficient between each sensor measured data and load, judgement is every
One sensor is with the presence or absence of linear abnormal: if linearly dependent coefficient is greater than 0.8, judging the sensor, there is no linear different
Often；If it is linear abnormal that linearly dependent coefficient less than 0.8, judges that the sensor occurs；For there is no linear abnormal biographies
Sensor calculates proportionality coefficient between its measured data and load, obtains actual measurement proportionality coefficient, then it is calculated with numerical value
Index contrast obtains actual measurement scale factor errors using numerical result as standard: if actual measurement scale factor errors are greater than
5%, then it is assumed that there are abnormal proportions for sensor；If surveying scale factor errors less than 5%, then it is assumed that sensor is normal；
Software kit monitoring interface prompts sensor there are exception and shows that abnormality sensor is numbered, while relevant linear exception
Sensing data is shown as black, and abnormal proportion sensing data is shown as brown, and nominal sensor data are shown as blue；
Step 4: selecting next step by testing crew according to the sensor abnormality judging result of step 3；If necessary to adjust
Sensor then enters step five；If you do not need to adjustment sensor, then start formally to load, enter step six；
Step 5: adjustment abnormality sensor；Two are entered step after adjustment, are preloaded again；
Step 6: formal load, obtains real-time measuring data；
Gradually apply load by test plan, and records sensor real time data；
Step 7: working sensor state analysis；
Monitoring system is according to each sensing data real-time change situation of the real-time data analysis obtained in step 6；By adopting
The linearly dependent coefficient between each sensor measured data and load is calculated with method identical in step 3, judges each
Sensor states: if linearly dependent coefficient is greater than threshold value, judge that sensor states are normal；If linearly dependent coefficient is less than
Threshold value judges that the sensor breaks down；The threshold size depends on test specimen type, loading type actual conditions；If deposited
In sensor, institute's measured data reaches its specified range in the case where without failure, then it is assumed that the sensor failure, data
The data on column appear dimmed, and no longer calculate the linearly dependent coefficient between its measured data and load；Simultaneously in step 8
In be not counted in fault sensor, but still be included in sensor sum in；
Step 8: judging sensor states grade；
Analysis based on step 7 calculates sensing system health value, and foundation as a result, count the working condition of each sensor
Table 1 determines sensor states grade；
1 sensor states of table determine and automatically process Measures Standard
It is required that: the standard for meeting multiple sensor states grades is subject to the highest level reached；Grade number is smaller,
Locating rank is higher；Loading procedure according to the ratio between load type, the numerical intervals of load, load and specified maximum load section
Etc. standards be divided into each loaded segment, according to test demand self-defining；For firsts and seconds sensor states grade, sensor
Threshold value S in system health value criterion11、S12、S13、S21、S22、S23... it can be soft in mating monitoring according to test demand
Self-defining in part；Wherein, the S11、S12、S13、S21、S22、S23... it is respectively less than 100；And there is S11≥S12≥S13, S21≥
S22≥S23, S11<S21, S12<S22, S13<S23；
Step 9: according to sensor states grade, Adjustment Tests process；
If sensor states grade is level-one, software kit monitoring interface prompt sensor states grade simultaneously shows that failure passes
Sensor number, the data on associated sensor data column are shown in red, are simultaneously emitted by level-one sound-light alarm, pilot system immediately from
It is dynamic to stop load；If sensor states grade is second level, software kit monitoring interface prompt sensor states grade is simultaneously shown
Show that fault sensor is numbered, the data on associated sensor data column are shown as orange, are simultaneously emitted by second level sound-light alarm, test system
System continues to load；If sensor states grade is three-level, software kit monitoring interface prompt sensor states grade is simultaneously shown
Show that fault sensor is numbered, the data on associated sensor data column are shown as yellow, and pilot system continues to load；If sensor
State grade is level Four, then software kit monitoring interface is normal with blue font prompt sensor states, sensing data column
Data are shown as blue, and pilot system continues to load；When sensor states grade is second level, three-level or level Four, if needing to stop
Test, then be manually operated by testing crew；Specific sensor states grade and pilot system automatically process measure and are shown in Table 1.
2. a kind of sensor states method of real-time for structure load electrical testing inspection according to claim 1,
It is characterized in that, linearly dependent coefficient is calculated by following calculation formula in the step three, step 7:
Wherein, X is that all measured datas of a sensor are sequentially arranged the vector of composition, and Y is to test load on time
Between the vector that sequentially rearranges；Cov (X, Y) is X, and the covariance of Y, Var [X], Var [Y] are respectively X, the variance of Y.
3. a kind of sensor states method of real-time for structure load electrical testing inspection according to claim 1 or 2,
It is characterized in that, actual measurement scale factor errors described in step 3 are calculated by following calculation formula:
4. a kind of sensor states method of real-time for structure load electrical testing inspection according to claim 1 or 2,
It is characterized in that, sensing system health value described in step 8 is calculated by following calculation formula:
Sensing system health value=100- (FP×NP+FI×NI+FG×NG)
Wherein, FP、FI、FGRespectively represent single key area sensor, important area sensor, general area sensor it is strong
Health weight；NP、NI、NGRespectively represent the number of faults of key area sensor, important area sensor, general area sensor.
5. a kind of sensor states method of real-time for structure load electrical testing inspection according to claim 3,
It is characterized in that, sensing system health value described in step 8 is calculated by following calculation formula:
Sensing system health value=100- (FP×NP+FI×NI+FG×NG)
Wherein, FP、FI、FGRespectively represent single key area sensor, important area sensor, general area sensor it is strong
Health weight；NP、NI、NGRespectively represent the number of faults of key area sensor, important area sensor, general area sensor.
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